List Available Metrics
list_metricsRead-onlyIdempotent
Retrieve available Prometheus metrics with pagination and filtering options to identify and access monitoring data.
Instructions
List all available metrics in Prometheus with optional pagination support
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| filter_pattern | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- The main handler function for the 'list_metrics' tool. It fetches the list of metric names from Prometheus using the label/__name__/values endpoint, applies optional filtering by pattern, pagination with limit and offset, supports progress reporting via context, and returns a structured result with counts and pagination info.
async def list_metrics( limit: Optional[int] = None, offset: int = 0, filter_pattern: Optional[str] = None, ctx: Context | None = None ) -> Dict[str, Any]: """Retrieve a list of all metric names available in Prometheus. Args: limit: Maximum number of metrics to return (default: all metrics) offset: Number of metrics to skip for pagination (default: 0) filter_pattern: Optional substring to filter metric names (case-insensitive) Returns: Dictionary containing: - metrics: List of metric names - total_count: Total number of metrics (before pagination) - returned_count: Number of metrics returned - offset: Current offset - has_more: Whether more metrics are available """ logger.info("Listing available metrics", limit=limit, offset=offset, filter_pattern=filter_pattern) # Report progress if context available if ctx: await ctx.report_progress(progress=0, total=100, message="Fetching metrics list...") data = make_prometheus_request("label/__name__/values") if ctx: await ctx.report_progress(progress=50, total=100, message=f"Processing {len(data)} metrics...") # Apply filter if provided if filter_pattern: filtered_data = [m for m in data if filter_pattern.lower() in m.lower()] logger.debug("Applied filter", original_count=len(data), filtered_count=len(filtered_data), pattern=filter_pattern) data = filtered_data total_count = len(data) # Apply pagination start_idx = offset end_idx = offset + limit if limit is not None else len(data) paginated_data = data[start_idx:end_idx] result = { "metrics": paginated_data, "total_count": total_count, "returned_count": len(paginated_data), "offset": offset, "has_more": end_idx < total_count } if ctx: await ctx.report_progress(progress=100, total=100, message=f"Retrieved {len(paginated_data)} of {total_count} metrics") logger.info("Metrics list retrieved", total_count=total_count, returned_count=len(paginated_data), offset=offset, has_more=result["has_more"]) return result - src/prometheus_mcp_server/server.py:364-374 (registration)The @mcp.tool() decorator registers the list_metrics function as an MCP tool, providing a description and annotations for UI hints and metadata.
@mcp.tool( description="List all available metrics in Prometheus with optional pagination support", annotations={ "title": "List Available Metrics", "icon": "📋", "readOnlyHint": True, "destructiveHint": False, "idempotentHint": True, "openWorldHint": True } )